Benchmarks of 15 MLIPs show parameter count and training set size correlate with accuracy, architecture drives speed and memory, and explicit Coulomb terms provide no benefit.
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Independent learning of static and dynamic charges in water systems achieves comparable accuracy to coupled models with environment-dependent screening but at lower computational cost.
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
citing papers explorer
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Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations
Benchmarks of 15 MLIPs show parameter count and training set size correlate with accuracy, architecture drives speed and memory, and explicit Coulomb terms provide no benefit.
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Simultaneous Learning of Static and Dynamic Charges
Independent learning of static and dynamic charges in water systems achieves comparable accuracy to coupled models with environment-dependent screening but at lower computational cost.
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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.